Layer-specific wide-field calcium imaging of neocortical activity

Reviewer #1 (Public review):

Summary:

The authors develop alignment methods for layer-specific widefield calcium imaging in the mouse cortex. Under the assumption that the majority of the widefield signal originates at the level of the cell bodies, different cortical layers will appear at different locations in a top-down view as a function of the curvature of the mouse cortex. The authors develop software tools to correct for this, as well as depth-dependent source blurring. Finally, they apply these tools to investigate functional connectivity differences of different neuron types and find only subtle differences.

Strengths:

The work is technically strong, the experiments well executed, and the presentation clear.

Weaknesses:

One concern I have is that the central assumption underlying the rationale for the depth correction, namely that the source of the majority of the widefield signal is the cell body, may be incorrect. Layer 5 neurons have a dense axo-dendritic plexus very close to the surface of the cortex. Given the attenuation length of visible light in tissue, as well as our own measurements (https://elifesciences.org/articles/71476#fig6s1), I suspect that the majority of the widefield calcium signal originates in the superficial axo-dendritic plexus. The authors acknowledge this possibility, but there are a few simple measurements they could make to address this more directly. If indeed, as I suspect, the majority of the calcium signal originates in the first 50 um of tissue (even when imaging layer 5 neurons), the curvature correction is counterproductive, of course. The authors could test the effect of adding brain slices of varying thicknesses on top of e.g., a layer 2/3 widefield recording. If the authors are correct, and most of the signal is from cell bodies, this should, at most, attenuate the layer 2/3 recording to the level of a layer 5 recording. Anecdotally, while doing the measurements for the figure referenced above, we have done this experiment with a 100 um thick slice, and no quantifiable calcium responses remained.

Reviewer #2 (Public review):

Summary:

This manuscript by Lorenzo and colleagues presents wide-field cortical imaging data obtained from experiments conducted with three triple-transgenic mouse lines that specifically express the calcium sensor GCaMP6f in neurons of layers 2/3, 5, and 6 of the neocortex, respectively.

It first includes a methodological contribution aimed at optimizing the analysis of the acquired signals, taking into account both the geometry of the neocortex and photon scattering in the cortical tissue, which affect fluorescence signals differentially depending upon their cortical depth of origin.

In particular, they built upon the work previously published in eLife by Waters in 2024, which, based on a simulation of photon scattering using a Monte Carlo random-walk model, provided an estimate of the tissue volumes contributing to the fluorescence signals measured from the surface in several mouse lines expressing Gcamp in a layer-specific manner.

The authors here additionally performed empirical measurements of the point spread function at different cortical depths to determine spatial kernels to be used to deconvolve wide-field imaging data acquired from their three-layer-specific GCaMP6f-expressing mouse lines. They assess the added value of this deconvolution approach based on recordings of the cortical responses evoked by whisker stimulation in the barrel cortex, using lightly anesthetized, layer 2/3 and layer 5 GCaMP6f-expressing mice.

Altogether, these proposed methods aim at optimizing the registration of recorded signals on a common reference frame, allowing to compare cortical spatiotemporal dynamics recorded from distinct layer-specific GCaMP-expressing mice.

The manuscript further contains a more neurophysiological contribution, directly utilizing the proposed methods to perform a comparative layer-specific functional connectivity analysis from data collected with the 3 different mouse lines, while the mice were head-fixed below the macroscope.

Strengths:

Wide-field 1-photon functional optical imaging, which allows recording cortical spatiotemporal dynamics over a large portion of the dorsal neocortex in mice, has become a tool of choice to study how activity over a wide range of cortical areas is orchestrated in various behavioral contexts. The ever-increasing availability of transgenic mice exhibiting pan-cortical calcium- or voltage-dependent sensors within specific neuronal populations is generating a growing interest in these approaches among the neuroscientific community.

Nowadays, it is possible to image specifically the activity of excitatory neurons whose cell bodies are located in given cortical layers. However, interpreting fluorescence signals recorded from the surface while originating from deep layers proves difficult due to photon scattering, which reduces image definition, as previously established by Waters et al. (2024).

The ability to correct for this blurring effect and to place the recorded signals within a common frame of reference is therefore essential not only for comparing activity across layers but also for integrating findings across studies, thereby advancing our collective understanding of neocortical physiology.

In this sense, this work by Lorenzo and colleagues is definitely both timely and valuable.

Overall, the manuscript is clearly structured and well-written, and the figures are of excellent graphic quality.

The proposed approach to correct the blurring of the fluorescent signals, which increases with depth, by means of empirical measurements of point spread functions and deconvolution, seems pertinent and efficient.

Finally, the authors have collected evoked and spontaneous dynamics of calcium signals from 3 different layer-specific GCaMP mice, which in itself represents a substantial experimental effort, not least because of the need to generate the animals. Out of these data, they provide a unique comparative analysis of layer-specific functional connectivity.

Weaknesses:

To fully benefit a large community, some aspects of the proposed methodological advances need to be more detailed in the manuscript and potentially refined. For instance, it is very difficult to evaluate, given the tiny confocal images provided in Figure 1, the potential contribution of GCaMP signal from apical dendrites of layer V neurons in Rbp4-GCaMP6f mice. It is also difficult for the reader to assess the added value of the layer-specific reference maps, given that functional image registration relies on nonlinear transformations and limited detail is provided regarding the procedure used to realign the functional data with these maps (lines 465-467). It is not really clear how the illustrated "composite maps" and the "five functional spots" used for the registration are computed. In addition, one could question the choice of the large time windows used to generate these composite maps/functional landmarks. Since the early component of the evoked responses is more likely to reflect the location of the initial thalamocortical inputs, restricting the analysis to the early phase of the responses might improve the accuracy of primary cortical area identification. This concern regarding the time window used to define specific cortical representation areas may also be relevant to Figure 4, which illustrates the results of the proposed deconvolution approach used to correct for photon scattering (although the time windows used for these analyses are not specified).

With regard to Figure 4, the reader might wonder why the results are not illustrated similarly for the layer 6 mice. It would therefore be useful to clearly indicate whether these data are not shown because they were not collected, or because it proved impossible to identify single whisker representations, despite the proposed deconvolution procedure.

Regarding the analysis of layer specificity in terms of functional connectivity, the authors extensively use the term "resting-state" to describe the behavioral context of data collection, given that the animals were not engaged in a goal-directed task. However, because the mice were experiencing head fixation beneath a functional epifluorescence macroscope for only the second time, it is questionable whether this state can truly be classified as "resting." As indicated by the global quantification of body movements, the animals most likely alternated between quiet wakefulness and more active phases.

To allow the reader to accurately interpret the reported functional connectivity differences, the authors should at least provide a quantification of the time animals spent in the quiet versus active states, and assess whether these proportions were comparable between the different mouse lines. Another way to address this issue would be to perform functional connectivity analyses after splitting the data according to these two states based on body movement quantification, although it is difficult to assess the feasibility of this approach without knowing the temporal distribution of these states within the dataset.

This seems particularly important since differences in neural cross-regional correlation patterns have been linked to arousal levels, with a comparable optical imaging approach, by Shahsavarani and colleagues (Cell Reports, 2023), who compared initial and prolonged resting periods. In addition, the authors report here that layer differences in functional connectivity are more pronounced in regions associated with the default mode network, whose activity is likely to differ between quiet and active wakefulness.

Finally, given the richness of the dataset, it would be very interesting to assess how the proposed deconvolution approach affects PCA-ICA-based functional parcellation of spontaneous cortical activity (Reidl et al., NeuroImage, 2007; Makino et al., Neuron, 2017) and whether it enables cross-layer comparisons of independent cortical modules. Such supplementary analyses would substantially increase the impact of this work.

Reviewer #3 (Public review):

This paper provides valuable technical and theoretical validation of layer-specific wide-field imaging. Here, the authors use specific transgenic lines that provide layer-specific cell body expression (and some superficial dendrites). They then use deconvolution approaches and potentially more accurate atlases based on depth-dependent features to register and resolve what are layer-specific functional GCaMP signals.

In general, the work is extremely well done, and I have little specific criticism. I think the author should be commended for their creative solutions, including using the light source at different depths to measure apparent scattering and blurring, allowing them to incorporate the deconvolution approach.

Throughout the manuscript, they refer to the signals as layer-specific and, for the most part, conclude similar functional connectivity as in different layers with some noted exceptions. This is an outstanding resource for the community.

Major Comment:

I think they should add some caveats that the lines that they employ do contain dendrites that are in more superficial cortices. Could they make some estimates of signal contribution from these, say, layer 6 neuron superficial dendrites versus the deep somata? This clarification should be included in the abstract; maybe they could call these apparent somatic signals? Another way of doing this would be a Soma-targeted deep indicator, but this is probably beyond the scope of the paper.

Alternatively, how much of the layer 5 signal would be expected to be recovered?